Quantifying reproducibility in computational biology: the case of the tuberculosis drugome.

How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published pap...

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Autores principales:	Daniel Garijo, Sarah Kinnings, Li Xie, Lei Xie, Yinliang Zhang, Philip E Bourne, Yolanda Gil
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/454d1ddf94184285835e1293a52f3b80
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spelling	oai:doaj.org-article:454d1ddf94184285835e1293a52f3b802021-11-18T08:44:27ZQuantifying reproducibility in computational biology: the case of the tuberculosis drugome.1932-620310.1371/journal.pone.0080278https://doaj.org/article/454d1ddf94184285835e1293a52f3b802013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24312207/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published paper for different classes of users (ranging from users with little expertise to domain experts) and suggest ways in which the situation might be improved. Quantification is achieved by estimating the time required to reproduce each of the steps in the method described in the original paper and make them part of an explicit workflow that reproduces the original results. Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results. The quantification leads to "reproducibility maps" that reveal that novice researchers would only be able to reproduce a few of the steps in the method, and that only expert researchers with advance knowledge of the domain would be able to reproduce the method in its entirety. The workflow itself is published as an online resource together with supporting software and data. The paper concludes with a brief discussion of the complexities of requiring reproducibility in terms of cost versus benefit, and a desiderata with our observations and guidelines for improving reproducibility. This has implications not only in reproducing the work of others from published papers, but reproducing work from one's own laboratory.Daniel GarijoSarah KinningsLi XieLei XieYinliang ZhangPhilip E BourneYolanda GilPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e80278 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daniel Garijo Sarah Kinnings Li Xie Lei Xie Yinliang Zhang Philip E Bourne Yolanda Gil Quantifying reproducibility in computational biology: the case of the tuberculosis drugome.
description	How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published paper for different classes of users (ranging from users with little expertise to domain experts) and suggest ways in which the situation might be improved. Quantification is achieved by estimating the time required to reproduce each of the steps in the method described in the original paper and make them part of an explicit workflow that reproduces the original results. Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results. The quantification leads to "reproducibility maps" that reveal that novice researchers would only be able to reproduce a few of the steps in the method, and that only expert researchers with advance knowledge of the domain would be able to reproduce the method in its entirety. The workflow itself is published as an online resource together with supporting software and data. The paper concludes with a brief discussion of the complexities of requiring reproducibility in terms of cost versus benefit, and a desiderata with our observations and guidelines for improving reproducibility. This has implications not only in reproducing the work of others from published papers, but reproducing work from one's own laboratory.
format	article
author	Daniel Garijo Sarah Kinnings Li Xie Lei Xie Yinliang Zhang Philip E Bourne Yolanda Gil
author_facet	Daniel Garijo Sarah Kinnings Li Xie Lei Xie Yinliang Zhang Philip E Bourne Yolanda Gil
author_sort	Daniel Garijo
title	Quantifying reproducibility in computational biology: the case of the tuberculosis drugome.
title_short	Quantifying reproducibility in computational biology: the case of the tuberculosis drugome.
title_full	Quantifying reproducibility in computational biology: the case of the tuberculosis drugome.
title_fullStr	Quantifying reproducibility in computational biology: the case of the tuberculosis drugome.
title_full_unstemmed	Quantifying reproducibility in computational biology: the case of the tuberculosis drugome.
title_sort	quantifying reproducibility in computational biology: the case of the tuberculosis drugome.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/454d1ddf94184285835e1293a52f3b80
work_keys_str_mv	AT danielgarijo quantifyingreproducibilityincomputationalbiologythecaseofthetuberculosisdrugome AT sarahkinnings quantifyingreproducibilityincomputationalbiologythecaseofthetuberculosisdrugome AT lixie quantifyingreproducibilityincomputationalbiologythecaseofthetuberculosisdrugome AT leixie quantifyingreproducibilityincomputationalbiologythecaseofthetuberculosisdrugome AT yinliangzhang quantifyingreproducibilityincomputationalbiologythecaseofthetuberculosisdrugome AT philipebourne quantifyingreproducibilityincomputationalbiologythecaseofthetuberculosisdrugome AT yolandagil quantifyingreproducibilityincomputationalbiologythecaseofthetuberculosisdrugome
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Quantifying reproducibility in computational biology: the case of the tuberculosis drugome.

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